Evidence of Increased Vehicle Speeding in Ohio’s Major Cities during the COVID-19 Pandemic

Lee, Jinhyung; Porr, Adam; Miller, Harvey · 2020 · OpenAlex-citations

DOI: 10.32866/001c.12988

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Summary

This study investigates whether vehicle speeding increased in Ohio’s three major urbanized areas—Columbus, Cincinnati, and Cleveland—during the early stages of the COVID-19 pandemic. Motivated by anecdotal reports of dramatic speeding increases alongside reduced traffic volumes due to stay-at-home orders, the authors sought to provide statistical evidence for this phenomenon. The research addresses two primary questions: whether the spatial extent of speeding expanded compared to the same period in the previous year, and whether the average differences in speeding levels were statistically significant. The authors note that if lower traffic volumes correlate with higher speeds, the risk of crashes and severity of outcomes may increase. The analysis utilizes high-resolution, real-time INRIX traffic data, specifically focusing on INRIX XD (eXtreme Definition) road segments, which offer granular coverage of 138,166 segments across Ohio. The study compares data from a three-week period in 2019 (March 28 to April 19) against the same period in 2020, following Ohio’s stay-at-home order effective March 23, 2020. Speeding is defined as the difference between the average observed speed and the INRIX reference speed, which approximates typical free-flow speeds. The methodology involves visualizing spatial patterns of speeding pre- and post-pandemic, generating summary statistics, and conducting bootstrap T-tests to assess the statistical significance of mean differences, accounting for non-normal data distributions. The findings reveal a significant increase in both the spatial extent and average level of speeding across all three cities. The proportion of road kilometers exhibiting speeding tripled in Columbus (from 20.6% to 70.6%), Cincinnati (from 21.5% to 63.5%), and Cleveland (from 22.5% to 63.9%). Average speeding levels also rose substantially; for instance, Columbus saw an increase from 4.49 km/h to 17.65 km/h, while Cincinnati and Cleveland increased from 6.23 to 15.70 km/h and 8.10 to 12.23 km/h, respectively. Density curves indicate a shift toward higher speeding distributions. Bootstrap T-tests confirmed these differences were statistically significant, with p-values less than 0.01 for all three urban areas. The study concludes that reduced traffic volumes during the pandemic led to increased vehicular speeding, suggesting that traffic density is a major controlling factor for vehicle speeds. This behavior renders streets and highways potentially more dangerous, implying a need for enhanced vigilance and enforcement measures, such as temporary speeding cameras. The authors also note that spatial variations in speeding may be influenced by street designs that encourage higher speeds. While the study assumes reduced traffic levels, it acknowledges the lack of direct traffic volume data as a limitation, suggesting future research should explore the specific relationships between traffic volume reductions and speeding patterns.

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• speed choice

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• Empirical Findings: observational prevalence